The Problem

Artificial intelligence continues to evolve, but the supporting infrastructure required to operationalize AI Agents is incomplete. While models have advanced in capability, the systems needed to build, deploy, scale, and monetize agents remain fragmented—creating significant friction for businesses, creators, and users.

AI solutions today suffer from the following structural limitations:

• Agents operate in isolation Most AI agents are built to perform single, narrow tasks within closed platforms. There is no native ability for agents to coordinate, share memory, or operate as part of larger intelligent systems. This prevents them from scaling into real-world, multi-step workflows that require collaboration or specialization.

• Limited customization and control Tools that offer accessible agent creation often stop at surface-level personalization. Users lack the ability to deeply configure behavior, logic, data scope, or tool integration—limiting the agent’s relevance to specific roles, domains, or industries. Customization typically requires technical development, which many users can’t support.

• Fragmented and inflexible deployment Deploying an agent into production environments—such as websites, internal dashboards, chat systems, or external apps—remains technically complex. There is no standardized deployment flow, and most platforms are tied to specific interfaces or require manual setup, delaying time-to-value and limiting reach.

• No infrastructure for data ingestion Agents need access to the right data to function effectively, yet most platforms lack integrated pipelines for feeding agents structured knowledge. Whether from files, cloud platforms, third-party tools, or internal documentation, data ingestion is often manual and error-prone, leading to shallow or inconsistent performance.

• No support for agent collaboration Current systems lack the architecture for multi-agent interaction. There’s no native framework for assigning roles, coordinating across domains, or managing agent-to-agent communication. Without this, AI is locked into isolated task execution, rather than functioning as a distributed system.

• No pathway to distribution or reuse Builders and teams who create valuable agents have no platform-level way to publish, license, or scale them. Every agent is a one-off, and the absence of a standardized marketplace means solutions can’t be shared, monetized, or reused. This stifles innovation and prevents the formation of a true ecosystem.

• Lack of analytics and visibility Once deployed, most agents operate as black boxes. Users lack insight into usage patterns, failure points, or optimization opportunities. Without analytics, there is no structured way to measure performance, iterate intelligently, or scale based on what’s working.

These limitations create a system where AI agents remain disconnected from real operations—difficult to build, harder to deploy, and nearly impossible to scale or share. The gap between what AI models can do, and what users can operationalize, remains wide.

Agently is designed to close this infrastructure gap—by enabling AI Agents to function like real software: composable, deployable, collaborative, and monetizable from the start.